标题： 可重复的效果量比不可重复的假设检验更适用于分析高通量测序数据集 显示英文标题

标题： A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets

摘要： 动机：从零假设显著性检验框架得出的P值受样本量的影响很大，并且在统计功效不足的研究中已被证明不可重复，但尚未提出合适的替代方法。 结果：在此，我们展示了针对高通量测序数据集的非参数标准化中位效应大小估计值dNEF的实现。 展示了转录组和标签测序数据集的案例研究。 结果显示，dNEF度量比P值更具可重复性和稳健性，并且所需的样本量小至3即可可重复地识别出差异丰富的特征。 可用性：源代码和二进制文件可在以下位置免费获得：https://bioconductor.org/packages/ALDEx2.html ，omicplotR，以及https://github.com/ggloor/CoDaSeq 。 显示英文摘要

摘要： Motivation: P values derived from the null hypothesis significance testing framework are strongly affected by sample size, and are known to be irreproducible in underpowered studies, yet no suitable replacement has been proposed. Results: Here we present implementations of non-parametric standardized median effect size estimates, dNEF, for high-throughput sequencing datasets. Case studies are shown for transcriptome and tag-sequencing datasets. The dNEF measure is shown to be more reproducible and robust than P values and requires sample sizes as small as 3 to reproducibly identify differentially abundant features. Availability: Source code and binaries freely available at: https://bioconductor.org/packages/ALDEx2.html , omicplotR, and https://github.com/ggloor/CoDaSeq .